System and method for content-based media analysis
First Claim
1. A media analysis system comprising:
- one or more hardware processors;
a memory storing synopses associated with a plurality of catalog books, each catalog book in the plurality of catalog books including a different synopsis; and
a content analysis engine, executable by the one or more hardware processors, configured to perform operations comprising;
generating a different media vector for each catalog book of the plurality of catalog books based on the synopsis of the catalog book, the generating comprising;
generating a word vector for each word of a plurality of words in the synopsis of the catalog book, thereby generating a plurality of word vectors;
combining the plurality of word vectors into a mean vector for the catalog book, the mean vector being the media vector; and
storing the mean vector, in the memory, as the media vector associated with the catalog book;
identifying a target book, the target book associated with a seed media vector;
determining R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books;
clustering the R nearest neighbors for the target book into K clusters; and
selecting a second plurality of catalog books for recommendation to a user based on the K clusters.
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Abstract
A media analysis system includes one or more hardware processors, a memory storing synopses associated with catalog books, and a content analysis engine. The content analysis engine generates a media vector for each catalog book based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the catalog book, and storing the mean vector as the media vector associated with the catalog book. The content analysis engine also identifies a target book associated with a seed media vector, determines R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books, clusters the R nearest neighbors into K clusters, and selects catalog books for recommendation to a user based on the K clusters.
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Citations
20 Claims
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1. A media analysis system comprising:
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one or more hardware processors; a memory storing synopses associated with a plurality of catalog books, each catalog book in the plurality of catalog books including a different synopsis; and a content analysis engine, executable by the one or more hardware processors, configured to perform operations comprising; generating a different media vector for each catalog book of the plurality of catalog books based on the synopsis of the catalog book, the generating comprising; generating a word vector for each word of a plurality of words in the synopsis of the catalog book, thereby generating a plurality of word vectors; combining the plurality of word vectors into a mean vector for the catalog book, the mean vector being the media vector; and storing the mean vector, in the memory, as the media vector associated with the catalog book; identifying a target book, the target book associated with a seed media vector; determining R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books; clustering the R nearest neighbors for the target book into K clusters; and selecting a second plurality of catalog books for recommendation to a user based on the K clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method for content-based media analysis, the method comprising:
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generating a different media vector for each catalog book of a plurality of catalog books based on a synopsis of the catalog book, the generating comprising; generating a word vector for each word of a plurality of words in the synopsis of the catalog book, thereby generating a plurality of word vectors; combining the plurality of word vectors into a mean vector for the catalog book, the mean vector being the media vector; and storing the mean vector, in a memory, as the media vector associated with the catalog book; identifying a target book, the target book associated with a seed media vector; determining R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books; clustering the R nearest neighbors for the target book into K clusters; and selecting a second plurality of catalog books for recommendation to a user based on the K clusters. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory machine-readable medium storing processor-executable instructions which, when executed by a processor, cause the processor to:
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generate a different media vector for each catalog book of a plurality of catalog books based on a synopsis of the catalog book, the generating comprising; generating a word vector for each word of a plurality of words in the synopsis of the catalog book, thereby generating a plurality of word vectors; combining the plurality of word vectors into a mean vector for the catalog book, the mean vector being the media vector; and storing the mean vector, in a memory, as the media vector associated with the catalog book; identify a target book, the target book is associated with a seed media vector; determine R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books; cluster the R nearest neighbors for the target book into K clusters; and select a second plurality of catalog books for recommendation to a user based on the K clusters. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification